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D5.4 Continuous Deep Learning Toolkit for Real Time Adaptation II Ecosystem for COllaborative Manufacturing PrOceSses – Intra- and Interfactory Integration and AutomaTION (Grant Agreement No 723145) D5.4 Continuous Deep Learning Toolkit for Real Time Adaptation II Date: 2019-02-27 Version 1.0 Published by the COMPOSITION Consortium Dissemination Level: Public Co-funded by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 723145 COMPOSITION D5.4 Continuous Deep Learning Toolkit for Real Time Adaptation II Document control page Document file: D5.4 Continuous deep learning toolkit for real time adaptation II v1.0.docx Document version: 1.0 Document owner: ISMB Work package: WP5 – Key Enabling Technologies for Intra- and Interfactory Interoperability and Data Analysis Task: T5.2 – Continuous Deep Learning Toolkit for real time adaptation Deliverable type: O Document status: Approved by the document owner for internal review Approved for submission to the EC Document history: Version Author(s) Date Summary of changes made 0.1 Paolo Vergori (LINKS) 29-11-2018 Definition of table of contents 0.8 Paolo Vergori, Nadir Raimondo, Luigi 19-02-2019 Updated according to the new development Giugliano (LINKS) 0.9 Luigi Giugliano (LINKS) 25-02-2019 Merged the review of Peter Haigh (TNI-UCC) 1.0 Luigi Giugliano (LINKS) 27-02-2019 Merged the review of Nacho González (ATOS) Internal review history: Reviewed by Date Summary of comments Peter Haigh (TNI-UCC) 23-02-2019 Approved with comments Nacho González (ATOS) 27-02-2019 Approved with minor comments Legal Notice The information in this document is subject to change without notice. The Members of the COMPOSITION Consortium make no warranty of any kind with regard to this document, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The Members of the COMPOSITION Consortium shall not be held liable for errors contained herein or direct, indirect, special, incidental or consequential damages in connection with the furnishing, performance, or use of this material. Possible inaccuracies of information are under the responsibility of the project. This report reflects solely the views of its authors. The European Commission is not liable for any use that may be made of the information contained therein. Document version: 1.0 Page 2 of 120 2019-02-27 COMPOSITION D5.4 Continuous Deep Learning Toolkit for Real Time Adaptation II 1 Executive Summary ................................................................................................................. 4 2 Abbreviations and acronyms .................................................................................................. 5 3 Introduction .............................................................................................................................. 6 3.1 Summary .......................................................................................................................... 6 3.2 Background ...................................................................................................................... 6 4 State-Of-The-Art analysis ........................................................................................................ 7 4.1 Intro to machine learning .................................................................................................. 7 4.2 Intro to neural network and deep learning ........................................................................ 8 4.3 Deep Feed Forward NN ................................................................................................... 8 4.4 Recurrent neural network and long-short term memory network ..................................... 9 5 Frameworks comparison .......................................................................................................10 5.1 Introduction to deep learning frameworks ......................................................................10 5.2 Evaluation criteria ...........................................................................................................10 5.3 Comparison ....................................................................................................................11 6 Inter and Intra-factory end users’ historical data assessment ..........................................19 6.1 Predictive maintenance (UC-BSL-2) ..............................................................................20 6.1.1 Background ..............................................................................................................20 6.1.2 Data Overview .........................................................................................................21 6.1.3 Fitness for usage .....................................................................................................24 6.1.4 Data assessment .....................................................................................................25 6.2 Prices and logistics (UC-ELDIA-1) .................................................................................25 6.2.1 Background ..............................................................................................................25 6.2.2 Data Overview .........................................................................................................26 6.2.3 Fitness for usage .....................................................................................................27 6.2.4 Data assessment .....................................................................................................27 7 Deep Learning Toolkit design ...............................................................................................28 7.1 Supervised learning results on price prediction from UC-ELDIA-1 ................................28 7.1.1 Eldia goods prices estimation with continuous learning ..........................................28 7.1.2 Eldia goods prices estimation with additional features ............................................31 7.2 Supervised learning results on predictive maintenance from UC-BSL-2 .......................32 7.2.1 Brady oven ...............................................................................................................32 7.2.2 Rhythmia oven .........................................................................................................39 7.2.3 Audio data preliminary analysis and solutions .........................................................47 7.3 Comparison of CPU and GPU training ...........................................................................48 8 Deep Learning Toolkit deployment ......................................................................................51 9 Conclusions ............................................................................................................................55 10 Annex I ....................................................................................................................................56 10.1 Unfitted use cases ..........................................................................................................56 10.1.1 Maintenance Decision Support (UC-KLE-1) .......................................................56 10.1.2 Fill level classification use cases (UC-ELDIA-1 UC-ELDIA-2) ...........................57 10.2 Deep Learning Toolkit preliminary design and testing ...................................................57 10.2.1 Introduction .........................................................................................................57 10.2.2 Feed Forward NN in TensorFlow........................................................................58 10.2.3 Feed Forward NN in H2O ....................................................................................64 10.2.4 Recurrent NN for time series regression ............................................................68 11 Annex II .................................................................................................................................100 12 List of Figures and Tables ...................................................................................................118 12.1 Figures ..........................................................................................................................118 12.2 Tables ...........................................................................................................................119 13 References ............................................................................................................................120 Document version: 1.0 Page 3 of 120 2019-02-27 COMPOSITION D5.4 Continuous Deep Learning Toolkit for Real Time Adaptation II 1 Executive Summary The present document named “D5.4 Continuous deep learning toolkit for real time adaptation II.docx” is a public deliverable of the COMPOSITION project, co-funded by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 723145. It reports the final results of task “5.2 – Continuous Deep Learning Toolkit for real time adaptation” that foresees its development in work package 5 “Key Enabling Technologies for Intra- and Interfactory Interoperability and Data”. The document owner is ISMB and in this final version is a reiteration of the submitted at M16, namely D5.3 Continuous deep learning toolkit for real time adaptation I. This version highlights the final results after a second iteration of task 5.2, regarding
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